class_docstr: Base class of stacking classifiers
docstr: Predict class confidence scores for X.
  
  Parameters
  ----------
  X : {array-like, sparse matrix}, shape = [n_samples, n_features]
      Training vectors, where n_samples is the number of samples and
      n_features is the number of features.
  
  Returns
  ----------
  scores : shape=(n_samples,) if n_classes == 2 else             (n_samples, n_classes).
      Confidence scores per (sample, class) combination. In the binary
      case, confidence score for self.classes_[1] where >0 means this
      class would be predicted.
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code: def decision_function(self, X):
      """ Predict class confidence scores for X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape = [n_samples, n_features]
              Training vectors, where n_samples is the number of samples and
              n_features is the number of features.
  
          Returns
          ----------
          scores : shape=(n_samples,) if n_classes == 2 else \
              (n_samples, n_classes).
              Confidence scores per (sample, class) combination. In the binary
              case, confidence score for self.classes_[1] where >0 means this
              class would be predicted.
  
          """
      check_is_fitted(self, ["clfs_", "meta_clf_"])
  
      return self._do_predict(X, self.meta_clf_.decision_function)
  
file_path: mlxtend/mlxtend/classifier/_base_classification.py
imports: import numpy
  from scipy import sparse
  from sklearn.base import ClassifierMixin
  from ..externals.estimator_checks import check_is_fitted
label_desc: Use image node_id 4 for calling the _BaseStackingClassifier obj's underlying member method code with example usage: obj.decision_function(X) and returns: self